1. Field of the Invention
This invention relates generally to anonymization of facial images, for example as may be used to develop training sets for machine learning.
2. Description of the Related Art
A facial expression is a visible manifestation of the affective state, cognitive activity, intention, personality, and/or psychopathology of a person. Facial expressions convey non-verbal communication cues in face-to-face interactions. These cues may also complement speech by helping the listener to elicit the intended meaning of spoken words. As a consequence of the information they carry, facial expressions not only help in interpersonal communications but also play an important role whenever humans interact with machines.
Automatic recognition of facial expressions may act as a component of natural human-machine interfaces. Such interfaces could enable the automated provision of services that require a good appreciation of the emotional state of the person receiving the services, as would be the case in transactions that involve negotiations. Some robots can also benefit from the ability to recognize facial expressions. Automated analysis of facial expressions for behavior science or medicine is another possible application domain.
One approach for developing automatic facial expression recognition systems relies on supervised machine learning using training sets. Training sets typically include facial images of human subjects and corresponding labels for the facial expression (e.g., whether the human subject is happy, sad, angry, surprised, etc.). Many examples from a wide range of human subjects (e.g., male, female, old, young, Asian, Caucasian, etc.) and different image rendering conditions (e.g., different cameras, different types of illumination, etc.) are desirable to train an AFER system to work reliably.
One way to obtain a large number of examples is to search the internet. However, many internet databases have pictures only of a certain group of similar-looking people (e.g., young female Caucasians), and using these examples as input to train an AFER system may result in overfitting. Moreover, the majority of the images found on the internet are unlabeled (i.e., without a facial expression category label), and labeling these images can be very labor-intensive and time-consuming. An alternative way to obtain examples from a wide range of people is to ask people to provide them (e.g., provide a picture of his/her face together with a corresponding facial expression category label). People may be willing to provide images of their own faces if, after some kind of modification to these images, they are no longer recognizable from these modified images. That is, human subjects may prefer that these images are “anonymized.” Such an anonymized image should preserve at least part of the emotional expression of the original facial image (i.e., information about facial expression) to be useful as an input to train an AFER system.
Therefore, there is a need for improved techniques to generate anonymized facial images.